library("tidyverse")
## ── Attaching packages ──────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ─────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
download.file(url = "https://ndownloader.figshare.com/files/2292169",
              destfile = "data/surveys_complete.csv")
surveys_complete <- read_csv("data/surveys_complete.csv")
## Parsed with column specification:
## cols(
##   record_id = col_double(),
##   month = col_double(),
##   day = col_double(),
##   year = col_double(),
##   plot_id = col_double(),
##   species_id = col_character(),
##   sex = col_character(),
##   hindfoot_length = col_double(),
##   weight = col_double(),
##   genus = col_character(),
##   species = col_character(),
##   taxa = col_character(),
##   plot_type = col_character()
## )
## To build a ggplot, we will use the following basic template that can be used for different types of plots:

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
  geom_point()
## Warning: Removed 4048 rows containing missing values (geom_point).

### To add a geom to the plot use + operator. Because we have two continuous variables, let’s use geom_point() first:

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
  geom_point()
## Warning: Removed 4048 rows containing missing values (geom_point).

Challenges

## To use hexagonal binning with ggplot2, first install the R package hexbin from CRAN:

install.packages("hexbin")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
library("hexbin")
surveys_plot <- ggplot(data = surveys_complete, 
                       mapping = aes(x = weight, y = hindfoot_length))
## Then use the geom_hex() function:

surveys_plot +
 geom_hex()
## Warning: Removed 4048 rows containing non-finite values (stat_binhex).

## Building plots with ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
    geom_point()
## Warning: Removed 4048 rows containing missing values (geom_point).

## Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha) to avoid overplotting:

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1)
## Warning: Removed 4048 rows containing missing values (geom_point).

## We can also add colors for all the points:
ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1, color = "orange")
## Warning: Removed 4048 rows containing missing values (geom_point).

##  where we color with species_id:
ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1, aes(color = species_id))
## Warning: Removed 4048 rows containing missing values (geom_point).

### Challenge 1

## Use what you just learned to create a scatter plot of weight over species_id with the plot types showing in different colors. Is this a good way to show this type of data?

ggplot(data = surveys_complete, 
       mapping = aes(x = species_id, y = weight)) +
   geom_point(aes(color = plot_type))
## Warning: Removed 2503 rows containing missing values (geom_point).

Boxplots

## We can use boxplots to visualize the distribution of weight within each species:

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_boxplot()
## Warning: Removed 2503 rows containing non-finite values (stat_boxplot).

##By adding points to the boxplot, we can have a better idea of the number of measurements and of their distribution:

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_boxplot(alpha = 0) +
    geom_jitter(alpha = 0.3, color = "tomato")
## Warning: Removed 2503 rows containing non-finite values (stat_boxplot).
## Warning: Removed 2503 rows containing missing values (geom_point).

## Replace the box plot with a violin plot; see geom_violin().

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_violin()
## Warning: Removed 2503 rows containing non-finite values (stat_ydensity).

## Represent weight on the log10 scale; see scale_y_log10()

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_violin() +
  scale_y_log10()
## Warning: Removed 2503 rows containing non-finite values (stat_ydensity).

## Try making a new plot to explore the distribution of another variable within each species.

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = hindfoot_length)) +
    geom_boxplot() +
    geom_jitter(aes(color = "plot_id"))
## Warning: Removed 3348 rows containing non-finite values (stat_boxplot).
## Warning: Removed 3348 rows containing missing values (geom_point).

Plottting time series

## Let’s calculate number of counts per year for each genus. First we need to group the data and count records within each group:

yearly_counts <- surveys_complete %>%
  count(year, genus)
## Timelapse data can be visualized as a line plot with years on the x-axis and counts on the y-axis:

ggplot(data = yearly_counts, aes(x = year, y = n)) + geom_line()

##  We need to tell ggplot to draw a line for each genus by modifying the aesthetic function to include group = genus:

ggplot(data = yearly_counts, aes(x = year, y = n, group = genus)) +
    geom_line()

## We will be able to distinguish species in the plot if we add colors (using color also automatically groups the data):

ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) +
    geom_line()

## In the previous lesson, we saw how to use the pipe operator %>% to use different functions in a sequence and create a coherent workflow. We can also use the pipe operator to pass the data argument to the ggplot() function. The hard part is to remember that to build your ggplot, you need to use + and not %>%.

yearly_counts %>% 
    ggplot(mapping = aes(x = year, y = n, color = genus)) +
    geom_line()

## The pipe operator can also be used to link data manipulation with consequent data visualization.

yearly_counts_graph <- surveys_complete %>%
    count(year, genus) %>% 
    ggplot(mapping = aes(x = year, y = n, color = genus)) +
    geom_line()

yearly_counts_graph

###Faceting

## ggplot has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to make a time series plot for each species:

ggplot(data = yearly_counts, aes(x = year, y = n)) +
    geom_line() +
    facet_wrap(facets = vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

## Now we would like to split the line in each plot by the sex of each individual measured. To do that we need to make counts in the data frame grouped by year, genus, and sex:

 yearly_sex_counts <- surveys_complete %>%
                      count(year, genus, sex)
## We can now make the faceted plot by splitting further by sex using color (within a single plot):
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(facets =  vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

## You can also organise the panels only by rows (or only by columns):
# One column, facet by rows

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

# One row, facet by column
ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(cols = vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

##ggplot_themes

## we can change our previous graph to have a simpler white background using the theme_bw() function:

 ggplot(data = yearly_sex_counts, 
        mapping = aes(x = year, y = n, color = sex)) +
     geom_line() +
     facet_wrap(vars(genus)) +
     theme_bw()
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

ggplot(data = yearly_sex_counts, 
        mapping = aes(x = year, y = n, color = sex)) +
     geom_line() +
     facet_wrap(vars(genus)) +
     theme_dark()
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

###Challenge1

##Use what you just learned to create a plot that depicts how the average weight of each species changes through the years.

yearly_weight <- surveys_complete %>%
                group_by(year, species_id) %>%
                 summarize(avg_weight = mean(weight))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
#> `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(data = yearly_weight, mapping = aes(x=year, y=avg_weight)) +
   geom_line() +
   facet_wrap(vars(species_id)) +
   theme_void()
## Warning: Removed 89 row(s) containing missing values (geom_path).
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

## Now, let’s change names of axes to something more informative than ‘year’ and ‘n’ and add a title to the figure:

ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
         x = "Year of observation",
         y = "Number of individuals") +
    theme_dark()
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

## The axes have more informative names, but their readability can be improved by increasing the font size. This can be done with the generic theme() function:
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(text=element_text(size = 16))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

## We can also modify the facet label text (strip.text) to italicize the genus names:

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                        axis.text.y = element_text(colour = "grey20", size = 12),
                        strip.text = element_text(face = "italic"),
                        text = element_text(size = 16))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

## If you like the changes you created better than the default theme, you can save them as an object to be able to easily apply them to other plots you may create:

grey_theme <- theme(axis.text.x = element_text(colour="grey20", size = 12, 
                                               angle = 90, hjust = 0.5, 
                                               vjust = 0.5),
                    axis.text.y = element_text(colour = "grey20", size = 12),
                    text=element_text(size = 16))

ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) +
    geom_boxplot() +
    grey_theme
## Warning: Removed 3348 rows containing non-finite values (stat_boxplot).

## Use the RStudio ggplot2 cheat sheet for inspiration.

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_dark() +
    theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                        axis.text.y = element_text(colour = "grey20", size = 7),
                        strip.text = element_text(face = "italic"),
                        text = element_text(size = 11))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

Application written in R [@RCoreTeam] using the Shiny framework [@Chang2015]. ### References

@Misc{Chang2015,
  Title                    = {shiny: Web Application Framework for R. R package version 0.12.1},
  Author                   = {Chang, W. and Cheng, J. and Allaire, JJ. and Xie, Y. and McPherson, J. },
  Year                     = {2015},
  Type                     = {Computer Program},
  Url                      = {http://CRAN.R-project.org/package=shiny}
}
@Article{RCoreTeam,
  Title                    = {R: A Language and Environment for Statistical Computing},
  Author                   = {{R Core Team}},
  Year                     = {2015},
  Type                     = {Journal Article},
  Url                      = {http://www.R-project.org}
}